{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Import all Packages"
   ],
   "metadata": {
    "id": "ZfX9I4zwNdM9"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vBzwpSkOtPAe"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Preparation\n",
    "1. We will be using the model `deit_base_patch16_224`\n",
    "  * This means all image need to be reshaped to (224,224)\n",
    "2. In training, we are using data augmentation process: `RandomHorizontalFlip`. \n",
    "  * It is optional step\n",
    "  *  It randomly flips an input image horizontally\n",
    "  * It  artificially increase the size and diversity of the training dataset\n",
    "3. We also normalizes the image `transforms.Normalize`\n",
    "  * This will reduce the scale of image\n",
    "  * It helps on Faster convergence, better generalization, and numercial stability\n"
   ],
   "metadata": {
    "id": "-hieKe9SfUWN"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Please download the images from `https://www.kaggle.com/datasets/jr2ngb/cataractdataset` and place in your machine's directory\n",
    "DATA_DIR = \"/Users/premtimsina/Documents/bpbbook/chapter7/dataset/\"\n",
    "CLASSES = [\"normal\", \"cataract\", \"glaucoma\", \"retina_disease\"]\n",
    "\n",
    "data = []\n",
    "for class_idx, class_name in enumerate(CLASSES):\n",
    "    class_dir = os.path.join(DATA_DIR, class_name)\n",
    "    for img_name in os.listdir(class_dir):\n",
    "        img_path = os.path.join(class_dir, img_name)\n",
    "        data.append([img_path, class_idx])\n",
    "\n",
    "df = pd.DataFrame(data, columns=[\"image_path\", \"label\"])\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42, stratify=df[\"label\"])\n",
    "\n",
    "train_transforms = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "])\n",
    "\n",
    "test_transforms = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "])\n",
    "\n",
    "class CataractDataset(Dataset):\n",
    "    def __init__(self, image_paths, labels, transform=None):\n",
    "        self.image_paths = image_paths\n",
    "        self.labels = labels\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.image_paths)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_path = self.image_paths[idx]\n",
    "        label = self.labels[idx]\n",
    "        img = Image.open(img_path).convert(\"RGB\")\n",
    "\n",
    "        if self.transform:\n",
    "            img = self.transform(img)\n",
    "\n",
    "        return img, label\n",
    "\n",
    "train_dataset = CataractDataset(train_df[\"image_path\"].values, train_df[\"label\"].values, transform=train_transforms)\n",
    "test_dataset = CataractDataset(test_df[\"image_path\"].values, test_df[\"label\"].values, transform=test_transforms)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=4, shuffle=False)\n",
    "\n"
   ],
   "metadata": {
    "id": "Onl2qdgttxX6"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Let's review our dataloader\n",
    "1. We are just viewing the image from train_dataloader\n",
    "2. There is one very important step:\n",
    "  * matplotlib need the image in [H, W, C]; where dataloader has image of shape [C, H,W]\n",
    "  * Thus, before plotting we are transposing so that the data is suitable for viewing for matplotlib"
   ],
   "metadata": {
    "id": "-1253g1TPL6i"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "data_iter = iter(train_loader)\n",
    "images, labels = next(data_iter)\n",
    "\n",
    "# Function to unnormalize and convert a tensor image to numpy array\n",
    "def imshow(img_tensor):\n",
    "    img = img_tensor.numpy()\n",
    "    # transposing\n",
    "    img = np.transpose(img, (1, 2, 0)) \n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    img = std * img + mean\n",
    "    img = np.clip(img, 0, 1)\n",
    "    return img\n",
    "\n",
    "# Display the images and their labels\n",
    "fig, axes = plt.subplots(1, len(images), figsize=(12, 12))\n",
    "\n",
    "for idx, (image, label) in enumerate(zip(images, labels)):\n",
    "    axes[idx].imshow(imshow(image))\n",
    "    axes[idx].set_title(f\"Label: {label.item()}\")\n",
    "    axes[idx].axis(\"off\")\n",
    "\n",
    "plt.show()\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 245
    },
    "id": "rD9vNXxBLM2g",
    "outputId": "8055b9de-99f0-4bdd-eb53-4dcd77b8f009"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Declare the model\n",
    "1. We are using the pre-trained model from timm\n",
    "2. If the image is color, \n",
    "  * in_chans=3;\n",
    "3. if image is graysclae, \n",
    "  * in_chans=3=1\n",
    "4. We need to declare, how many classes we have in out dataset: \n",
    "  * For our case, it was 4.\n",
    "  * num_classes=4\n",
    "5. `pretrained=True`\n",
    "  * We are mentioning that we want weight of pre-trained model. If you want to train from scratch, you could just mention \n",
    "    * pretrained=False"
   ],
   "metadata": {
    "id": "ySxcYUlrQCyh"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import timm\n",
    "import timm\n",
    "\n",
    "model = timm.create_model(\"deit_base_patch16_224\", pretrained=True)\n",
    "\n",
    "# Change the output size of the final linear layer to match the number of classes\n",
    "num_classes = 4\n",
    "model.head = torch.nn.Linear(model.head.in_features, num_classes)\n"
   ],
   "metadata": {
    "id": "48WE3Cmntu_M",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "6a9eea3d-2349-4dbd-f425-ca8d270fd41a"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Downloading: \"https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth\" to /Users/premtimsina/.cache/torch/hub/checkpoints/deit_base_patch16_224-b5f2ef4d.pth\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Declare the train and test loop\n",
    "1. In the below code: I am converting output and target into 'cpu'. Somehow my M1 chip is producing the problem. You may not encounter the problem. Thus, this is totally optional step."
   ],
   "metadata": {
    "id": "4o3rjbj4QLGT"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def train(model, device, train_loader, optimizer, criterion, epoch, accelerator):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "\n",
    "    for batch_idx, (data, target) in enumerate(tqdm(train_loader)):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = criterion(output, target)\n",
    "        accelerator.backward(loss)\n",
    "        optimizer.step()\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    avg_loss = running_loss / len(train_loader)\n",
    "    print(f\"Epoch: {epoch}, Loss: {avg_loss:.4f}\")\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, recall_score, precision_score\n",
    "\n",
    "def test(model, device, test_loader, criterion, accelerator):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    all_preds = []\n",
    "    all_targets = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += criterion(output, target).item()\n",
    "            output_cpu = output.to('cpu')\n",
    "            target_cpu=target.to('cpu')\n",
    "            pred = output_cpu.argmax(dim=1, keepdim=True)\n",
    "            correct += pred.eq(target_cpu.view_as(pred)).sum().item()\n",
    "            \n",
    "            all_preds.extend(pred.flatten().tolist())\n",
    "            all_targets.extend(target.flatten().tolist())\n",
    "\n",
    "    test_loss /= len(test_loader)\n",
    "    accuracy = 100. * correct / len(test_loader.dataset)\n",
    "    print(f\"Test Loss: {test_loss:.4f}, Accuracy: {accuracy:.2f}%\")\n",
    "    \n",
    "    # Calculate confusion matrix, sensitivity (recall), and specificity\n",
    "    cm = confusion_matrix(all_targets, all_preds)\n",
    "    sensitivity = recall_score(all_targets, all_preds, average=None)\n",
    "    specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n",
    "    \n",
    "    for i, (sens, spec) in enumerate(zip(sensitivity, specificity)):\n",
    "        print(f\"Class {i}: Sensitivity (Recall): {sens:.4f}, Specificity: {spec:.4f}\")\n",
    "\n"
   ],
   "metadata": {
    "id": "LiSLKNCqt48G"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from accelerate import Accelerator\n",
    "from torch.optim import Adam\n",
    "\n",
    "accelerator = Accelerator()\n",
    "device = accelerator.device\n",
    "learning_rate = 1e-4\n",
    "\n",
    "optimizer = Adam(model.parameters(), lr=learning_rate)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "train_loader, test_loader = accelerator.prepare(train_loader, test_loader)\n",
    "model, optimizer, criterion = accelerator.prepare(model, optimizer, criterion)\n",
    "\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "id": "nTzHALszt_0I"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "\n",
    "\n",
    "num_epochs = 10\n",
    "for epoch in range(1, num_epochs + 1):\n",
    "    train(model, device, train_loader, optimizer, criterion, epoch, accelerator)\n",
    "    test(model, device, test_loader, criterion, accelerator)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M26lZZr66wWE",
    "outputId": "b8605452-3198-4123-9dea-d3b487c35952"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:11<00:00,  1.68it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 1, Loss: 1.1989\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_62321/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
      "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test Loss: 1.3381, Accuracy: 35.54%\n",
      "Class 0: Sensitivity (Recall): 0.2623, Specificity: 0.2000\n",
      "Class 1: Sensitivity (Recall): 0.9500, Specificity: 0.7564\n",
      "Class 2: Sensitivity (Recall): 0.4000, Specificity: 0.6522\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.69it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 2, Loss: 1.0950\n",
      "Test Loss: 1.1172, Accuracy: 40.50%\n",
      "Class 0: Sensitivity (Recall): 0.2787, Specificity: 0.4138\n",
      "Class 1: Sensitivity (Recall): 0.6000, Specificity: 0.1429\n",
      "Class 2: Sensitivity (Recall): 0.9000, Specificity: 0.7534\n",
      "Class 3: Sensitivity (Recall): 0.1000, Specificity: 0.6000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:11<00:00,  1.69it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 3, Loss: 1.0110\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_62321/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
      "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test Loss: 1.0449, Accuracy: 57.85%\n",
      "Class 0: Sensitivity (Recall): 0.7213, Specificity: 0.3803\n",
      "Class 1: Sensitivity (Recall): 0.4500, Specificity: 0.1000\n",
      "Class 2: Sensitivity (Recall): 0.8500, Specificity: 0.5750\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.71it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 4, Loss: 0.8916\n",
      "Test Loss: 0.8551, Accuracy: 66.12%\n",
      "Class 0: Sensitivity (Recall): 0.8033, Specificity: 0.3288\n",
      "Class 1: Sensitivity (Recall): 0.7000, Specificity: 0.4167\n",
      "Class 2: Sensitivity (Recall): 0.7500, Specificity: 0.2500\n",
      "Class 3: Sensitivity (Recall): 0.1000, Specificity: 0.5000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.71it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 5, Loss: 0.9009\n",
      "Test Loss: 0.8480, Accuracy: 66.12%\n",
      "Class 0: Sensitivity (Recall): 0.8525, Specificity: 0.3333\n",
      "Class 1: Sensitivity (Recall): 0.7500, Specificity: 0.3478\n",
      "Class 2: Sensitivity (Recall): 0.6000, Specificity: 0.3684\n",
      "Class 3: Sensitivity (Recall): 0.0500, Specificity: 0.0000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.71it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 6, Loss: 0.7908\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_62321/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
      "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test Loss: 0.8915, Accuracy: 61.16%\n",
      "Class 0: Sensitivity (Recall): 0.7541, Specificity: 0.3867\n",
      "Class 1: Sensitivity (Recall): 0.7500, Specificity: 0.3478\n",
      "Class 2: Sensitivity (Recall): 0.6500, Specificity: 0.4348\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:11<00:00,  1.69it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 7, Loss: 0.7330\n",
      "Test Loss: 0.8084, Accuracy: 68.60%\n",
      "Class 0: Sensitivity (Recall): 0.8361, Specificity: 0.2917\n",
      "Class 1: Sensitivity (Recall): 0.7500, Specificity: 0.2500\n",
      "Class 2: Sensitivity (Recall): 0.7500, Specificity: 0.4444\n",
      "Class 3: Sensitivity (Recall): 0.1000, Specificity: 0.0000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.70it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 8, Loss: 0.6984\n",
      "Test Loss: 0.9093, Accuracy: 57.85%\n",
      "Class 0: Sensitivity (Recall): 0.6557, Specificity: 0.3651\n",
      "Class 1: Sensitivity (Recall): 0.6500, Specificity: 0.0714\n",
      "Class 2: Sensitivity (Recall): 0.6500, Specificity: 0.5667\n",
      "Class 3: Sensitivity (Recall): 0.2000, Specificity: 0.7143\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.70it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 9, Loss: 0.5615\n",
      "Test Loss: 0.9382, Accuracy: 63.64%\n",
      "Class 0: Sensitivity (Recall): 0.8033, Specificity: 0.3553\n",
      "Class 1: Sensitivity (Recall): 0.8000, Specificity: 0.3600\n",
      "Class 2: Sensitivity (Recall): 0.6000, Specificity: 0.3684\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: 1.0000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:10<00:00,  1.70it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 10, Loss: 0.6045\n",
      "Test Loss: 0.9643, Accuracy: 66.94%\n",
      "Class 0: Sensitivity (Recall): 0.9344, Specificity: 0.3133\n",
      "Class 1: Sensitivity (Recall): 0.7000, Specificity: 0.1765\n",
      "Class 2: Sensitivity (Recall): 0.4000, Specificity: 0.4286\n",
      "Class 3: Sensitivity (Recall): 0.1000, Specificity: 0.7143\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Analysis:\n",
    "1. The loss is decreasing. We need more epoch."
   ],
   "metadata": {
    "id": "OmuIiGVBQtsM"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Resnet-50"
   ],
   "metadata": {
    "id": "QBFg68058yBc"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm"
   ],
   "metadata": {
    "id": "1ZSTQVQn9r2U"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# Load ResNet-50 model\n",
    "resnet_model = timm.create_model(\"resnet50\", pretrained=True)\n",
    "\n",
    "# Change the output size of the final linear layer for ResNet-50 to match the number of classes\n",
    "resnet_model.fc = torch.nn.Linear(resnet_model.fc.in_features, num_classes)"
   ],
   "metadata": {
    "id": "kr7E1x71HAgW"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from accelerate import Accelerator\n",
    "from torch.optim import Adam\n",
    "\n",
    "accelerator = Accelerator()\n",
    "device = accelerator.device\n",
    "learning_rate = 1e-4\n",
    "\n",
    "optimizer = Adam(model.parameters(), lr=learning_rate)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "train_loader, test_loader = accelerator.prepare(train_loader, test_loader)\n",
    "resnet_model, optimizer, criterion = accelerator.prepare(resnet_model, optimizer, criterion)"
   ],
   "metadata": {
    "id": "P0-lzN_B95sB"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "num_epochs = 10\n",
    "for epoch in range(1, num_epochs + 1):\n",
    "    train(resnet_model, device, train_loader, optimizer, criterion, epoch, accelerator)\n",
    "    test(resnet_model, device, test_loader, criterion, accelerator)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SUQOcN-Z-NGQ",
    "outputId": "40029c5b-9596-486c-be18-cb7695bda577"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:07<00:00,  1.77it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 1, Loss: 1.3756\n",
      "Test Loss: 1.4105, Accuracy: 28.93%\n",
      "Class 0: Sensitivity (Recall): 0.3115, Specificity: 0.4062\n",
      "Class 1: Sensitivity (Recall): 0.5000, Specificity: 0.8000\n",
      "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.8421\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: 1.0000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:08<00:00,  1.76it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 2, Loss: 1.3777\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_62321/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
      "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test Loss: 1.3482, Accuracy: 33.88%\n",
      "Class 0: Sensitivity (Recall): 0.3607, Specificity: 0.2667\n",
      "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.7609\n",
      "Class 2: Sensitivity (Recall): 0.4000, Specificity: 0.8222\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:08<00:00,  1.75it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 3, Loss: 1.3763\n",
      "Test Loss: 1.3525, Accuracy: 33.06%\n",
      "Class 0: Sensitivity (Recall): 0.4098, Specificity: 0.3243\n",
      "Class 1: Sensitivity (Recall): 0.6000, Specificity: 0.7692\n",
      "Class 2: Sensitivity (Recall): 0.1500, Specificity: 0.9032\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: 1.0000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:09<00:00,  1.74it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 4, Loss: 1.3787\n",
      "Test Loss: 1.3787, Accuracy: 29.75%\n",
      "Class 0: Sensitivity (Recall): 0.3443, Specificity: 0.4000\n",
      "Class 1: Sensitivity (Recall): 0.5000, Specificity: 0.7959\n",
      "Class 2: Sensitivity (Recall): 0.2500, Specificity: 0.8611\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: 1.0000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:08<00:00,  1.76it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 5, Loss: 1.3759\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_62321/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
      "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test Loss: 1.4205, Accuracy: 29.75%\n",
      "Class 0: Sensitivity (Recall): 0.2951, Specificity: 0.3793\n",
      "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.7843\n",
      "Class 2: Sensitivity (Recall): 0.3500, Specificity: 0.8293\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:08<00:00,  1.76it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 6, Loss: 1.3739\n",
      "Test Loss: 1.3405, Accuracy: 32.23%\n",
      "Class 0: Sensitivity (Recall): 0.3770, Specificity: 0.4250\n",
      "Class 1: Sensitivity (Recall): 0.5000, Specificity: 0.7297\n",
      "Class 2: Sensitivity (Recall): 0.2500, Specificity: 0.8810\n",
      "Class 3: Sensitivity (Recall): 0.0500, Specificity: 0.5000\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:08<00:00,  1.76it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 7, Loss: 1.3761\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_62321/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
      "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test Loss: 1.3409, Accuracy: 33.06%\n",
      "Class 0: Sensitivity (Recall): 0.3443, Specificity: 0.4167\n",
      "Class 1: Sensitivity (Recall): 0.6500, Specificity: 0.7347\n",
      "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.8333\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:08<00:00,  1.74it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 8, Loss: 1.3759\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_62321/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
      "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test Loss: 1.3343, Accuracy: 35.54%\n",
      "Class 0: Sensitivity (Recall): 0.4262, Specificity: 0.4222\n",
      "Class 1: Sensitivity (Recall): 0.5500, Specificity: 0.7381\n",
      "Class 2: Sensitivity (Recall): 0.3000, Specificity: 0.8235\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:08<00:00,  1.75it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 9, Loss: 1.3765\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/var/folders/dt/v286l_1506x752dg9dwh_yl40000gn/T/ipykernel_62321/263114855.py:46: RuntimeWarning: invalid value encountered in divide\n",
      "  specificity = (cm.sum(axis=0) - cm.diagonal()) / cm.sum(axis=0)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Test Loss: 1.3672, Accuracy: 33.88%\n",
      "Class 0: Sensitivity (Recall): 0.4754, Specificity: 0.4082\n",
      "Class 1: Sensitivity (Recall): 0.5000, Specificity: 0.7619\n",
      "Class 2: Sensitivity (Recall): 0.1000, Specificity: 0.9333\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "100%|█████████████████████████████████████████| 120/120 [01:07<00:00,  1.77it/s]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 10, Loss: 1.3765\n",
      "Test Loss: 1.3480, Accuracy: 38.02%\n",
      "Class 0: Sensitivity (Recall): 0.4590, Specificity: 0.3488\n",
      "Class 1: Sensitivity (Recall): 0.4500, Specificity: 0.7353\n",
      "Class 2: Sensitivity (Recall): 0.4500, Specificity: 0.7907\n",
      "Class 3: Sensitivity (Recall): 0.0000, Specificity: 1.0000\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "mgI453VF-Uyp"
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}
